import os
import torch
import torchvision
from d2l import torch as d2l
from torch import nn as nn

# download data
d2l.DATA_HUB['dog_tiny'] = (d2l.DATA_URL + 'kaggle_dog_tiny.zip',
                            '0cb91d09b814ecdc07b50f31f8dcad3e81d6a86d')

# 如果使用Kaggle比赛的完整数据集，请将下面的变量更改为False
demo = True
if demo:
    data_dir = d2l.download_extract('dog_tiny')
else:
    data_dir = os.path.join('..', 'data', 'dog-breed-identification')


# format data and split check data
def reorg_dog_data(data_dir, valid_ratio):
    labels = d2l.read_csv_labels(os.path.join(data_dir, 'labels.csv'))
    d2l.reorg_train_valid(data_dir, labels, valid_ratio)
    d2l.reorg_test(data_dir)


batch_size = 32 if demo else 128
valid_ratio = 0.1
reorg_dog_data(data_dir, valid_ratio)

# image enhance - 图像增广
# 训练集处理
transform_train = torchvision.transforms.Compose([
    # 随机裁剪图像，所得图像为原始面积的0.08～1之间，高宽比在3/4和4/3之间。
    # 然后，缩放图像以创建224x224的新图像
    torchvision.transforms.RandomResizedCrop(224, scale=(0.08, 1.0),
                                             ratio=(3.0/4.0, 4.0/3.0)),
    torchvision.transforms.RandomHorizontalFlip(),
    # 随机更改亮度，对比度和饱和度
    torchvision.transforms.ColorJitter(brightness=0.4,
                                       contrast=0.4,
                                       saturation=0.4),
    # 添加随机噪声
    torchvision.transforms.ToTensor(),
    # 标准化图像的每个通道
    torchvision.transforms.Normalize([0.485, 0.456, 0.406],
                                     [0.229, 0.224, 0.225])])
# 测试级处理
transform_test = torchvision.transforms.Compose([
    torchvision.transforms.Resize(256),
    # 从图像中心裁切224x224大小的图片
    torchvision.transforms.CenterCrop(224),
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize([0.485, 0.456, 0.406],
                                     [0.229, 0.224, 0.225])])


#  load data
train_ds, train_valid_ds = [torchvision.datasets.ImageFolder(
    os.path.join(data_dir, 'train_valid_test', folder),
    transform=transform_train) for folder in ['train', 'train_valid']]

valid_ds, test_ds = [torchvision.datasets.ImageFolder(
    os.path.join(data_dir, 'train_valid_test', folder),
    transform=transform_test) for folder in ['valid', 'test']]

# data mount
train_iter, train_valid_iter = [torch.utils.data.DataLoader(
    dataset, batch_size, shuffle=True, drop_last=True)
    for dataset in (train_ds, train_valid_ds)]

valid_iter = torch.utils.data.DataLoader(valid_ds, batch_size, shuffle=False,
                                         drop_last=True)

test_iter = torch.utils.data.DataLoader(test_ds, batch_size, shuffle=False,
                                        drop_last=False)


# define net
def get_net(devices):
    finetune_net = nn.Sequential()
    finetune_net.features = torchvision.models.resnet34(pretrained=True)
    # 定义一个新的输出网络，共有120个输出类别
    finetune_net.output_new = nn.Sequential(nn.Linear(1000, 256),
                                            nn.ReLU(),
                                            nn.Linear(256, 120))
    # 将模型参数分配给用于计算的CPU或GPU
    finetune_net = finetune_net.to(devices[0])
    # 冻结参数
    for param in finetune_net.features.parameters():
        param.requires_grad = False
    return finetune_net


# loss define
loss = nn.CrossEntropyLoss(reduction='none')


def evaluate_loss(data_iter, net, devices):
    l_sum, n = 0.0, 0
    for features, labels in data_iter:
        features, labels = features.to(devices[0]), labels.to(devices[0])
        outputs = net(features)
        l = loss(outputs, labels)
        l_sum += l.sum()
        n += labels.numel()
    return (l_sum / n).to('cpu')


# train func
def train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,
            lr_decay):
    """训练函数"""
    # 只训练小型自定义输出网络
    net = nn.DataParallel(net, device_ids=devices).to(devices[0])
    trainer = torch.optim.SGD((param for param in net.parameters()
                               if param.requires_grad), lr=lr,
                              momentum=0.9, weight_decay=wd)
    scheduler = torch.optim.lr_scheduler.StepLR(trainer, lr_period, lr_decay)
    num_batches, timer = len(train_iter), d2l.Timer()
    legend = ['train loss']
    if valid_iter is not None:
        legend.append('valid loss')
    animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],
                            legend=legend)
    for epoch in range(num_epochs):
        metric = d2l.Accumulator(2)
        for i, (features, labels) in enumerate(train_iter):
            timer.start()
            features, labels = features.to(devices[0]), labels.to(devices[0])
            trainer.zero_grad()
            output = net(features)
            l = loss(output, labels).sum()
            l.backward()
            trainer.step()
            metric.add(l, labels.shape[0])
            timer.stop()
            if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
                animator.add(epoch + (i + 1) / num_batches,
                             (metric[0] / metric[1], None))
        measures = f'train loss {metric[0] / metric[1]:.3f}'
        if valid_iter is not None:
            valid_loss = evaluate_loss(valid_iter, net, devices)
            animator.add(epoch + 1, (None, valid_loss.detach().cpu()))
        scheduler.step()
        if valid_iter is not None:
            measures += f', valid loss {valid_loss:.3f}'
        print(measures + f'\n{metric[1] * num_epochs / timer.sum():.1f}'
                         f' examples/sec on {str(devices)}')


if __name__ == "__main__":
    devices, num_epochs, lr, wd = ['cpu'], 10, 1e-4, 1e-4
    lr_period, lr_decay, net = 2, 0.9, get_net(devices)
    train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,
          lr_decay)
